Authors - Usman Ali, Ghulam Mohayud Din, Sajid, Ayesha Ali, Munawar Hussain, Muhammad Mujeeb Akbar Abstract - The proliferation of misinformation on social media poses significant social, political and economic risks. This research proposes an AI-based fake news detection system that leverages deep learning (BERT and LSTM) and Explainable Artificial Intelligence (XAI) frameworks to classify online fake news as Fake or True. The proposed architecture processes textual data through Natural Language Processing (NLP) techniques for semantic and contextual analysis. To ensure Interpretability, SHAP and LIME is Integrated to visualize the rationale behind classification results. The system was trained using balanced datasets augmented through SMOTE, achieving over 95% accuracy. A web-based interface was developed to facilitate real-time text and URL verification, providing confidence scores and explanations. This approach minimizes human intervention, enhances transparency and explainable frameworks yields an accurate and trust-worthy tool for combating misinformation.
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